Due to recent development in technology, there is an increase in the usage of digital cameras, smartphone, and Internet.After the successful implementation of the abovementioned models, CBIR and feature extraction approaches are applied in various applications such as medical image analysis, remote sensing, crime detection, video analysis, military surveillance, and textile industry.To provide a query image as an input is the main requirement of CBIR and it matches the visual contents of query image with the images that are placed in the archive, and closeness in the visual similarity in terms of image feature vector provides a base to find images with similar contents.In CBIR, low-level visual features (e.g., color, shape, texture, and spatial layout) are computed from the query and matching of these features is performed to sort the output .According to the literature, Query-By-Image Content (QBIC) and SIMPLicity are the examples of image retrieval models that are based on the extraction of low-level visual semantic.Content-based image retrieval (CBIR) is a framework that can overcome the abovementioned problems as it is based on the visual analysis of contents that are part of the query image.The approaches based on automatic image annotation are dependent on how accurate a system is in detecting color, edges, texture, spatial layout, and shape-related information .